Advancements in self-supervised learning with deep learning

Self-supervised learning has emerged as a transformative paradigm in deep learning, enabling models to learn meaningful representations from unlabeled data. This paper delves into the principles, tasks, strategies, and applications of self-supervised learning within the context of deep learning. It provides an overview of key advancements, discusses various self-supervised tasks, explores architectural innovations, and showcases real-world applications. Through comprehensive analysis and case studies, this paper highlights the challenges and future directions of research in the field of self-supervised learning and demonstrates the growing significance of self-supervised learning in enhancing the capabilities of deep learning models.